

'''simple docstring'''






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__   		=      logging.get_logger(__name__)

lowerCAmelCase__   		=      '''▁'''

lowerCAmelCase__   		=      {'''vocab_file''': '''sentencepiece.bpe.model'''}

lowerCAmelCase__   		=      {
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

lowerCAmelCase__   		=      {
    '''xlm-roberta-base''': 512,
    '''xlm-roberta-large''': 512,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 512,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 512,
    '''xlm-roberta-large-finetuned-conll03-english''': 512,
    '''xlm-roberta-large-finetuned-conll03-german''': 512,
}






class      lowercase_      (lowerCamelCase__ ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    str							     =     VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE       :    List[str]							     =     PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE       :    List[str]							     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE       :    str							     =     ['input_ids', 'attention_mask']






  def __init__( self  :	str   ,lowercase__  :	Dict   ,lowercase__  :	int="<s>"   ,lowercase__  :	List[Any]="</s>"   ,lowercase__  :	Any="</s>"   ,lowercase__  :	Union[str, Any]="<s>"   ,lowercase__  :	str="<unk>"   ,lowercase__  :	Optional[int]="<pad>"   ,lowercase__  :	Dict="<mask>"   ,lowercase__  :	Optional[Dict[str, Any]] = None   ,**lowercase__  :	Tuple   ,):
   # Mask token behave like a normal word, i.e. include the space before it
   __lowercase				    =					AddedToken(lowercase__   ,lstrip=lowercase__   ,rstrip=lowercase__     ) if isinstance(lowercase__   ,lowercase__     ) else mask_token

   __lowercase				    =					{} if sp_model_kwargs is None else sp_model_kwargs

   super().__init__(
       bos_token=lowercase__   ,eos_token=lowercase__   ,unk_token=lowercase__   ,sep_token=lowercase__   ,cls_token=lowercase__   ,pad_token=lowercase__   ,mask_token=lowercase__   ,sp_model_kwargs=self.sp_model_kwargs   ,**lowercase__   ,)

   __lowercase				    =					spm.SentencePieceProcessor(**self.sp_model_kwargs     )
   self.sp_model.Load(str(lowercase__     )     )
   __lowercase				    =					vocab_file

   # Original fairseq vocab and spm vocab must be "aligned":
   # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
   # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
   # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
   # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

   # Mimic fairseq token-to-id alignment for the first 4 token
   __lowercase				    =					{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

   # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
   __lowercase				    =					1

   __lowercase				    =					len(self.sp_model     ) + self.fairseq_offset
   __lowercase				    =					{v: k for k, v in self.fairseq_tokens_to_ids.items()}






  def __getstate__( self  :	Optional[Any]     ):
   __lowercase				    =					self.__dict__.copy()
   __lowercase				    =					None
   __lowercase				    =					self.sp_model.serialized_model_proto()
   return state






  def __setstate__( self  :	List[Any]   ,lowercase__  :	Tuple     ):
   __lowercase				    =					d

   # for backward compatibility
   if not hasattr(self   ,'''sp_model_kwargs'''     ):
    __lowercase				    =					{}

   __lowercase				    =					spm.SentencePieceProcessor(**self.sp_model_kwargs     )
   self.sp_model.LoadFromSerializedProto(self.sp_model_proto     )






  def    SCREAMING_SNAKE_CASE ( self  :	Tuple   ,lowercase__  :	List[int]   ,lowercase__  :	Optional[List[int]] = None     ):

   if token_ids_a is None:
    return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
   __lowercase				    =					[self.cls_token_id]
   __lowercase				    =					[self.sep_token_id]
   return cls + token_ids_a + sep + sep + token_ids_a + sep






  def    SCREAMING_SNAKE_CASE ( self  :	Tuple   ,lowercase__  :	List[int]   ,lowercase__  :	Optional[List[int]] = None   ,lowercase__  :	bool = False     ):

   if already_has_special_tokens:
    return super().get_special_tokens_mask(
        token_ids_a=lowercase__   ,token_ids_a=lowercase__   ,already_has_special_tokens=lowercase__     )

   if token_ids_a is None:
    return [1] + ([0] * len(lowercase__     )) + [1]
   return [1] + ([0] * len(lowercase__     )) + [1, 1] + ([0] * len(lowercase__     )) + [1]






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]   ,lowercase__  :	List[int]   ,lowercase__  :	Optional[List[int]] = None     ):
   __lowercase				    =					[self.sep_token_id]
   __lowercase				    =					[self.cls_token_id]

   if token_ids_a is None:
    return len(cls + token_ids_a + sep     ) * [0]
   return len(cls + token_ids_a + sep + sep + token_ids_a + sep     ) * [0]






  @property
  def    SCREAMING_SNAKE_CASE ( self  :	str     ):
   return len(self.sp_model     ) + self.fairseq_offset + 1  # Add the <mask> token






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]     ):
   __lowercase				    =					{self.convert_ids_to_tokens(lowercase__     ): i for i in range(self.vocab_size     )}
   vocab.update(self.added_tokens_encoder     )
   return vocab






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[int]   ,lowercase__  :	str     ):
   return self.sp_model.encode(lowercase__   ,out_type=lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Dict   ,lowercase__  :	List[Any]     ):
   if token in self.fairseq_tokens_to_ids:
    return self.fairseq_tokens_to_ids[token]
   __lowercase				    =					self.sp_model.PieceToId(lowercase__     )

   # Need to return unknown token if the SP model returned 0
   return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






  def    SCREAMING_SNAKE_CASE ( self  :	List[str]   ,lowercase__  :	int     ):
   if index in self.fairseq_ids_to_tokens:
    return self.fairseq_ids_to_tokens[index]
   return self.sp_model.IdToPiece(index - self.fairseq_offset     )






  def    SCREAMING_SNAKE_CASE ( self  :	Tuple   ,lowercase__  :	Union[str, Any]     ):
   __lowercase				    =					''''''.join(lowercase__     ).replace(lowercase__   ,''' '''     ).strip()
   return out_string






  def    SCREAMING_SNAKE_CASE ( self  :	List[Any]   ,lowercase__  :	str   ,lowercase__  :	Optional[str] = None     ):
   if not os.path.isdir(lowercase__     ):
    logger.error(F"Vocabulary path ({save_directory}) should be a directory"     )
    return
   __lowercase				    =					os.path.join(
       lowercase__   ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']     )

   if os.path.abspath(self.vocab_file     ) != os.path.abspath(lowercase__     ) and os.path.isfile(self.vocab_file     ):
    copyfile(self.vocab_file   ,lowercase__     )
   elif not os.path.isfile(self.vocab_file     ):
    with open(lowercase__   ,'''wb'''     ) as fi:
     __lowercase				    =					self.sp_model.serialized_model_proto()
     fi.write(lowercase__     )

   return (out_vocab_file,)


from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      input("""Enter image url: """).strip()
     print(F'''Downloading image from {url} ...''')
     _SCREAMING_SNAKE_CASE 		=      BeautifulSoup(requests.get(url).content, """html.parser""")
     # The image URL is in the content field of the first meta tag with property og:image
     _SCREAMING_SNAKE_CASE 		=      soup.find("""meta""", {"""property""": """og:image"""})["""content"""]
     _SCREAMING_SNAKE_CASE 		=      requests.get(image_url).content
     _SCREAMING_SNAKE_CASE 		=      F'''{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'''
     with open(file_name, """wb""") as fp:
          fp.write(image_data)
     print(F'''Done. Image saved to disk as {file_name}.''')
